1. Field of the Invention
The present invention is related to a method for operating a telecom system, more in particular a wireless system, and devices suited therefor.
2. Discussion of Related Technology
By discussion of technologies and references in this section, Applicants do not admit that the references are prior art of the invention disclosed in this application.
The current demand in increasing data rate and quality of service in advanced wireless communications has to cope with an energy budget severely constrained by autonomy and ubiquity requirements. Trading off performance and energy consumption deserves the highest attention to enable the ‘anything, anywhere, anytime’ paradigm.
The following observations show the need to integrate the energy-efficient approaches across layers. First, state-of-the-art wireless systems devices are built to operate at only a fixed set of operating points and assume the worst-case conditions at all times. Irrespective of the link utilization, the highest feasible PHY rate is always used and the power amplifier operates at the maximum transmit power. Indeed, when using non-scalable transceivers, this highest feasible rate results in the smallest duty cycle for the power amplifier. Compared to scalable systems, this results in excessive energy consumption for average channel conditions and average link utilizations. However, recent energy-efficient wireless system designs focus on energy-efficient VLSI implementations and adaptive physical layer algorithms to adjust the modulation, code rate or transmission power. For these schemes to be practical, they need to be aware of the instantaneous user requirements.
Further, to realize sizeable energy savings, systems need to shutdown the components when inactive. This is achieved only by tightly coupling the MAC to be able to communicate traffic requirements of a single user and schedule shutdown intervals.
In the case of a multi-user wireless communication system, there exist complex trade-offs between the adaptive physical layer schemes and the requirements of multiple users. For example, lowering the rate of one user affects the available time for the second delay sensitive user. This forces the second user to increase its rate, consume more energy and potentially suffer from a higher bit error rate.
However, the traditional approaches, including most of the state-of-the-art cross-layer optimization frameworks, do not yet enable a meaningful trade-off between performance and energy consumption. Indeed, most of them solve problems in an ad hoc way, focusing on the interaction between adjacent layers and do not raise the scope to the user level. Indeed, the real performance metrics are those quantifying the quality of the service provided by the complete communication stack to the application, while the only effective energy consciousness indicator is the actual energy that is drained from the battery. Both depend jointly on the propagation aspects, the physical layer, the complete protocol stack, the application itself and, more problematically, also on their implementation aspects. This spans far more than the scope of traditional system optimization approaches. Furthermore, the traditional ‘optimization paradigm’ itself, namely finding a unique optimal communication system configuration representing the best trade-off between performance and cost, becomes inappropriate when the dynamics in the wireless environment and user requirements are considered. More specifically, because of this dynamics, no unique optimal working point exists. The ultimate energy-efficient system would have to adapt permanently its characteristic, given the environment constraints, to provide the performance exactly required by the user with the minimum energy.
To achieve this goal flexible systems must be specified having so-called configuration knobs that can be set at run-time to steer jointly the performance and energy consumption. The higher the flexibility, i.e. the number of configuration knobs across all layers, the higher the potential gain due to a better match between the system behavior, the environment and the real user requirements. However, a penalty exists due to the required shift, at least partially, of the optimization process to run-time. This is very challenging due to the combinatorial character of the problem (the number of candidate configurations rises exponentially with the number of controlled knobs).
Recently, joint transmission power and rate control has been considered to reduce system power (see D. Qiao et al., ‘Energy Efficient PCF Operation of IEEE802.11a WLAN with Transmit Power control’, Elsevier Computer Networks (ComNet), vol. 42, no. 1, pp. 39-54, May 2003 and ‘MiSer: An Optimal Low-Energy transmission Strategy for IEEE 802.11a/h’, Proc. ACM MobiCom '03, San Diego, September 2003). This approach can be seen as the application to wireless system design of the ‘power aware’ design paradigm proposed by Sinha et al. (‘Energy Scalable System Design’, Trans. on VLSI Systems, April 2002, pp. 135-145). Given the fact that transmitting at a lower rate requires less power, the ‘lazy scheduling’ principle has been proposed (see ‘Adaptive Transmission for Energy Efficiency in Wireless Data Networks’, E. Uysal-Biyikoglu, Ph.D. Thesis, Stanford, June 2003): based on a look-ahead of the link utilization (i.e. packet arrival at the transmitter), the minimum average rate to satisfy the current traffic requirements is considered and transmit rate and power are set in function of the channel state in order to achieve this average during the next look-ahead window.
In ‘Energy-aware Wireless Communications’ (C. Schurgers, Ph.D. thesis, University of California, Los Angeles, 2002) the concept of energy-aware radio-management is developed. It proposes simple models to capture the energy consumption of radio systems that are used to derive some energy-efficient algorithms to select the modulation order, the code rate and to schedule the packets. This dynamic modulation and code scaling is proposed as a practical way to implement lazy scheduling. It also discusses the energy trade-off between transmission rates and shutting off the system. Operating regions are derived when a transceiver may sleep or use transmission scaling for time-invariant and time-varying channels. However, the general solutions to transparently exploit the energy-performance scalability at run-time are limited to a few (2-3) system level knobs. The energy-scalability of a system can be defined as the range in which the energy consumption can vary when the performance requirements—e.g. the user data rate—or the external constraints—e.g. the propagation conditions—vary from worst to best case.
In ‘Practical Lazy Scheduling in Sensor Networks’, R. Rao et al, Proc ACM Sensor Networks Conf, Los Angeles, November 2003 a CSMA/CA MAC protocol based on the lazy scheduling idea is derived.
From a theoretical point of view, the ‘lazy scheduling’ concept is attractive. E.g. radio link control based on ‘lazy scheduling’ looks to be a promising technique for WLAN power management. However, it has been analyzed exclusively from the viewpoint of physical, MAC and dynamic link control (DLC) layer. Yet, effective power management in radio communication requires considering the complete protocol stack and its cross-layer interactions.